Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "66"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 66 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 33 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 66, Node N03:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460016 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.258121 1.006882 7.147199 2.642795 0.397865 0.391747 6.971274 4.800138 0.5231 0.5731 0.3767 nan nan
2460015 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.438259 1.148960 7.127587 3.329596 0.303415 -0.017908 6.346841 4.556831 0.5370 0.5772 0.3712 nan nan
2460014 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.488469 0.669688 5.832324 2.712308 1.964993 -0.301367 3.373787 2.157787 0.4955 0.5580 0.3835 nan nan
2460013 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.693129 0.910561 7.157304 3.936419 1.811575 0.203114 8.724074 4.035604 0.5301 0.5770 0.3766 nan nan
2460012 digital_ok 100.00% 0.00% 0.00% 100.00% - - 0.886430 0.073533 -1.330778 4.367779 1.716211 2.060308 -0.118813 2.411242 0.2083 0.1848 -0.2802 nan nan
2460011 digital_ok 100.00% 0.00% 0.00% 100.00% - - 0.616255 0.039224 -1.938024 5.823270 2.922218 0.988799 -0.265985 1.594514 0.2117 0.1939 -0.2814 nan nan
2460010 digital_ok 100.00% 0.00% 0.00% 100.00% - - 0.721926 0.033851 -1.265837 5.090665 3.190727 1.214474 -0.454407 1.057411 0.2088 0.1892 -0.2804 nan nan
2460009 digital_ok 100.00% 0.00% 0.00% 100.00% - - 0.786350 0.279319 -1.514280 5.290002 2.020858 1.208512 -0.515070 1.714824 0.2171 0.2004 -0.2846 nan nan
2460008 digital_ok 100.00% 0.00% 0.00% 99.73% - - 0.741762 0.147496 -1.766950 5.498909 1.592803 2.406589 1.867143 4.734125 0.3062 0.2966 -0.2598 nan nan
2460007 digital_ok 100.00% 0.00% 0.00% 100.00% - - 1.014261 0.403471 -1.356767 4.091930 1.731271 0.910923 -0.396468 2.103736 0.2375 0.2233 -0.2788 nan nan
2459999 digital_ok 0.00% 100.00% 99.92% 0.00% - - nan nan nan nan nan nan nan nan 0.0871 0.1701 0.1286 nan nan
2459998 digital_ok 0.00% 0.00% 0.00% 99.95% - - 0.768669 0.661184 -1.179232 3.332378 2.374906 2.216138 -0.811748 1.507345 0.2107 0.1919 -0.3028 nan nan
2459997 digital_ok 0.00% 0.00% 0.00% 100.00% - - 0.637693 0.682477 -1.182420 3.578618 2.415052 1.052159 -0.301375 3.796874 0.2158 0.2021 -0.3073 nan nan
2459996 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.060278 1.918696 -1.326561 -0.366779 0.672145 0.008351 -0.532777 1.307832 0.6156 0.6315 0.4109 nan nan
2459995 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.912469 1.785144 -1.519616 -0.826908 3.049810 -0.199829 -0.646349 0.689381 0.6063 0.6220 0.4003 nan nan
2459994 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.442798 1.848814 -1.269593 -0.582097 2.076319 -0.153258 -0.556665 0.429122 0.6046 0.6186 0.3953 nan nan
2459993 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.199644 2.070633 -1.330678 -0.753458 2.684937 -0.348263 -0.470180 0.501636 0.5853 0.6258 0.4166 nan nan
2459991 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.171127 2.029376 -1.275756 -0.753086 2.403250 0.124337 -0.414832 0.364674 0.6183 0.6280 0.3998 nan nan
2459990 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.136232 1.665470 -1.129019 -0.838673 2.740796 0.143892 -0.207001 0.461286 0.6155 0.6281 0.3986 nan nan
2459989 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.662749 1.673397 -0.990700 -0.510851 2.458161 0.370699 -0.571593 0.073824 0.6127 0.6268 0.4003 nan nan
2459988 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.423693 2.134152 -1.265078 -0.992529 2.314810 0.008011 -0.673156 0.101080 0.6090 0.6247 0.3929 nan nan
2459987 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.126094 1.532421 -1.346781 -0.875264 1.650263 -0.131354 -0.437924 0.745485 0.6194 0.6329 0.3915 nan nan
2459986 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.049339 2.087282 -1.446838 -1.162032 2.464244 -0.269408 -0.757897 -1.250872 0.6404 0.6582 0.3428 nan nan
2459985 digital_ok 0.00% 0.00% 0.00% 0.00% - - 1.201080 2.121194 -1.400123 -0.948399 1.598057 -0.054327 -0.006813 0.800957 0.6162 0.6310 0.3984 nan nan
2459984 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.728089 1.757630 -1.322643 -0.765321 -0.510202 12.723418 -0.605465 1.197284 0.6263 0.6445 0.3808 nan nan
2459983 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.766469 1.619037 -1.284360 -1.115753 2.004645 -0.313592 -0.618665 -0.919318 0.6372 0.6631 0.3429 nan nan
2459982 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.316803 0.697573 -0.735851 -0.679512 1.843496 0.175713 -0.406328 -0.746889 0.6989 0.7053 0.2929 nan nan
2459981 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.647061 1.314357 -1.389505 -1.384671 2.548062 -0.642012 -0.716697 -0.424224 0.6165 0.6347 0.3962 nan nan
2459980 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.474431 1.102310 -1.434436 -1.116515 1.904206 -0.394150 -0.985730 -1.035818 0.6626 0.6779 0.3179 nan nan
2459979 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.478320 1.203975 -1.332878 -1.159263 2.212114 -0.285851 -0.812032 -0.530970 0.6088 0.6308 0.3983 nan nan
2459978 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.637866 1.260284 -1.409298 -1.287564 2.330524 -0.217539 -0.706679 -0.218388 0.6102 0.6304 0.4043 nan nan
2459977 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.634514 1.341861 -1.425297 -1.128046 2.141497 -0.764833 -0.721266 -0.271560 0.5725 0.5920 0.3686 nan nan
2459976 digital_ok 0.00% 0.00% 0.00% 0.00% - - 0.508577 1.028170 -1.290859 -1.436914 3.208537 -0.641899 -0.992429 -0.701754 0.6162 0.6362 0.3928 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 66: 2460016

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Power 7.147199 1.006882 0.258121 2.642795 7.147199 0.391747 0.397865 4.800138 6.971274

Antenna 66: 2460015

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Power 7.127587 1.148960 0.438259 3.329596 7.127587 -0.017908 0.303415 4.556831 6.346841

Antenna 66: 2460014

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Power 5.832324 0.488469 0.669688 5.832324 2.712308 1.964993 -0.301367 3.373787 2.157787

Antenna 66: 2460013

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Discontinuties 8.724074 0.693129 0.910561 7.157304 3.936419 1.811575 0.203114 8.724074 4.035604

Antenna 66: 2460012

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 4.367779 0.886430 0.073533 -1.330778 4.367779 1.716211 2.060308 -0.118813 2.411242

Antenna 66: 2460011

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 5.823270 0.616255 0.039224 -1.938024 5.823270 2.922218 0.988799 -0.265985 1.594514

Antenna 66: 2460010

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 5.090665 0.721926 0.033851 -1.265837 5.090665 3.190727 1.214474 -0.454407 1.057411

Antenna 66: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 5.290002 0.786350 0.279319 -1.514280 5.290002 2.020858 1.208512 -0.515070 1.714824

Antenna 66: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 5.498909 0.147496 0.741762 5.498909 -1.766950 2.406589 1.592803 4.734125 1.867143

Antenna 66: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 4.091930 1.014261 0.403471 -1.356767 4.091930 1.731271 0.910923 -0.396468 2.103736

Antenna 66: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 66: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Power 3.332378 0.768669 0.661184 -1.179232 3.332378 2.374906 2.216138 -0.811748 1.507345

Antenna 66: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Temporal Discontinuties 3.796874 0.637693 0.682477 -1.182420 3.578618 2.415052 1.052159 -0.301375 3.796874

Antenna 66: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Shape 1.918696 1.060278 1.918696 -1.326561 -0.366779 0.672145 0.008351 -0.532777 1.307832

Antenna 66: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 3.049810 0.912469 1.785144 -1.519616 -0.826908 3.049810 -0.199829 -0.646349 0.689381

Antenna 66: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.076319 1.442798 1.848814 -1.269593 -0.582097 2.076319 -0.153258 -0.556665 0.429122

Antenna 66: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.684937 1.199644 2.070633 -1.330678 -0.753458 2.684937 -0.348263 -0.470180 0.501636

Antenna 66: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.403250 1.171127 2.029376 -1.275756 -0.753086 2.403250 0.124337 -0.414832 0.364674

Antenna 66: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.740796 1.665470 1.136232 -0.838673 -1.129019 0.143892 2.740796 0.461286 -0.207001

Antenna 66: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.458161 1.673397 1.662749 -0.510851 -0.990700 0.370699 2.458161 0.073824 -0.571593

Antenna 66: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.314810 2.134152 1.423693 -0.992529 -1.265078 0.008011 2.314810 0.101080 -0.673156

Antenna 66: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 1.650263 1.126094 1.532421 -1.346781 -0.875264 1.650263 -0.131354 -0.437924 0.745485

Antenna 66: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.464244 2.087282 1.049339 -1.162032 -1.446838 -0.269408 2.464244 -1.250872 -0.757897

Antenna 66: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Shape 2.121194 2.121194 1.201080 -0.948399 -1.400123 -0.054327 1.598057 0.800957 -0.006813

Antenna 66: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok nn Temporal Variability 12.723418 0.728089 1.757630 -1.322643 -0.765321 -0.510202 12.723418 -0.605465 1.197284

Antenna 66: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.004645 0.766469 1.619037 -1.284360 -1.115753 2.004645 -0.313592 -0.618665 -0.919318

Antenna 66: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 1.843496 0.316803 0.697573 -0.735851 -0.679512 1.843496 0.175713 -0.406328 -0.746889

Antenna 66: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.548062 1.314357 0.647061 -1.384671 -1.389505 -0.642012 2.548062 -0.424224 -0.716697

Antenna 66: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 1.904206 1.102310 0.474431 -1.116515 -1.434436 -0.394150 1.904206 -1.035818 -0.985730

Antenna 66: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.212114 0.478320 1.203975 -1.332878 -1.159263 2.212114 -0.285851 -0.812032 -0.530970

Antenna 66: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.330524 1.260284 0.637866 -1.287564 -1.409298 -0.217539 2.330524 -0.218388 -0.706679

Antenna 66: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 2.141497 0.634514 1.341861 -1.425297 -1.128046 2.141497 -0.764833 -0.721266 -0.271560

Antenna 66: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
66 N03 digital_ok ee Temporal Variability 3.208537 1.028170 0.508577 -1.436914 -1.290859 -0.641899 3.208537 -0.701754 -0.992429
In [ ]: